sktime-cython 0.1.0

Cython extensions for sktime

sktime-cython

Cython-compiled estimators for sktime.

This package hosts ahead-of-time compiled implementations of sktime algorithms, isolating the C-compilation and binary-wheel complexity from the main sktime package. Estimators here expose a plain numpy-in/numpy-out compute layer with no sktime runtime dependency; sktime keeps thin BaseTransformer / BaseEstimator wrappers that delegate to this package.

Why a separate package: compiled extensions need a C toolchain, per-platform wheels, and cibuildwheel release machinery. Keeping that here lets sktime stay pure-Python while still offering compiled, numba-free fast paths.

Estimators

Estimator	Compute API	Notes
Multivariate MiniRocket	sktime_cython.fit / transform	numba-free; equivalent to MiniRocketMultivariate, no JIT warmup

More estimators are added as .pyx kernels under sktime_cython/_cython/ with a numpy compute layer alongside.

Each estimator lives in its own submodule (nothing is re-exported at the top level, so estimators never collide on common names like fit/transform):

import numpy as np
from sktime_cython.minirocket import fit, transform

X = np.random.rand(100, 6, 500).astype("float32")
params = fit(X, num_kernels=10_000, random_state=42)
features = transform(X, params, n_jobs=-1)   # GIL-released kernel, real threads

Runtime dependency: numpy only.

Development

Requires a C compiler and Python 3.10+. Run from the project root.

# editable install with the dev extra (pulls sktime + numba for equivalence
# tests, plus pre-commit); compiles the Cython extensions via build isolation
uv pip install -e ".[dev]"        # or: pip install -e ".[dev]"

# install the git pre-commit hooks ONCE — this is what stops CI lint failures
pre-commit install

# run the tests
python -m pytest sktime_cython -v

After editing a .pyx, recompile with pip install -e . again (or make build).

make shortcuts (auto-detect uv, falling back to pip/python): make install, make build, make test, make clean.

Before you push (avoid CI failures)

CI runs the pre-commit hooks and fails if they reformat anything. Running pre-commit install (above) auto-runs them on every git commit. To check the whole tree on demand:

pre-commit run --all-files

This runs ruff check (lint) and ruff format. If ruff format reports "files were modified", it already fixed them — git add the changes and commit again.

CI workflows

• test.yml (push / PR to main): a code-quality job running the pre-commit hooks, plus a matrix that builds the Cython extensions and runs pytest across Python 3.10–3.14 on Linux, macOS, and Windows.
• release.yml (on GitHub release): builds binary wheels with cibuildwheel for all platforms, builds an sdist (with .pyx sources), and publishes to PyPI via trusted publishing.

Adding an estimator

1. Drop the kernel .pyx (and optional .pyi stub) in a subfolder of the package. The package structure mirrors that of sktime, files should be added paralleling its primary import location in sktime
2. Register the kernel as an Extension in setup.py.
3. Add a numpy compute-layer module sktime_cython/<name>.py exposing the estimator's public functions. Keep them in the submodule — do not re-export at the top level (avoids name collisions across estimators).
4. Add tests in a subfolder tests, in the same folder as the kernel.

Package structure

Cython kernels and python layers should be added in a parallel location as in the sktime package.

Example for minirocket:

sktime_cython/
  transformations/
    rocket/
      __init__.py                           # public compute-layer exports
      _minirocket.py                        # numpy fit/transform layer
      _minirocket_multivariate_cython.pyx   # compiled kernels
      _minirocket_multivariate_cython.pyi   # type stubs
      tests/
        __init__.py
        test_minirocket.py                  # tests

License

BSD 3-Clause License — see LICENSE.